Physical-based optimization for non-physical image dehazing methods. Vazquez-Corral, J., Finlayson, G. D., & Bertalmío, M. Optics Express, 28(7):9327–9339, March, 2020.
Physical-based optimization for non-physical image dehazing methods [link]Paper  doi  abstract   bibtex   
Images captured under hazy conditions (e.g. fog, air pollution) usually present faded colors and loss of contrast. To improve their visibility, a process called image dehazing can be applied. Some of the most successful image dehazing algorithms are based on image processing methods but do not follow any physical image formation model, which limits their performance. In this paper, we propose a post-processing technique to alleviate this handicap by enforcing the original method to be consistent with a popular physical model for image formation under haze. Our results improve upon those of the original methods qualitatively and according to several metrics, and they have also been validated via psychophysical experiments. These results are particularly striking in terms of avoiding over-saturation and reducing color artifacts, which are the most common shortcomings faced by image dehazing methods.
@article{uea74616,
          volume = {28},
          number = {7},
           month = {March},
          author = {Javier Vazquez-Corral and Graham D. Finlayson and Marcelo Bertalm{\'i}o},
           title = {Physical-based optimization for non-physical image dehazing methods},
            year = {2020},
         journal = {Optics Express},
             doi = {10.1364/OE.383799},
           pages = {9327--9339},
        keywords = {framework},
             url = {https://ueaeprints.uea.ac.uk/id/eprint/74616/},
        abstract = {Images captured under hazy conditions (e.g. fog, air pollution) usually present faded colors and loss of contrast. To improve their visibility, a process called image dehazing can be applied. Some of the most successful image dehazing algorithms are based on image processing methods but do not follow any physical image formation model, which limits their performance. In this paper, we propose a post-processing technique to alleviate this handicap by enforcing the original method to be consistent with a popular physical model for image formation under haze. Our results improve upon those of the original methods qualitatively and according to several metrics, and they have also been validated via psychophysical experiments. These results are particularly striking in terms of avoiding over-saturation and reducing color artifacts, which are the most common shortcomings faced by image dehazing methods.}
}

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